- Poster presentation
- Open Access
Exploring the crystal structures of orientation maps in a scalable computational model of visual cortical maps
© Xiao et al; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Lattice Structure
- Natural Image
- Hexagonal Lattice
- Image Patch
- Cortical Slice
In this paper, we propose a new model for orientation maps based on the GCAL framework to address these limitations. Our model incorporates translation invariance into the network architecture. We enforce an additional constraint on GCAL, that neurons with a specific distance away from each other have identical afferent weights. The map is thus divided into regular zones with identical orientation preference layout, an equivalent of hypercolumns in V1. We test our model using the Topographica simulator . The network is trained on synthetic and natural image inputs. The orientation maps obtained show crystal-like regular lattice structures. Pinwheel centers emerge (Figure 1C, D), as found in V1. We implement hexagonal and square lattice structures. Compared to the hexagonal lattice maps, the square lattice maps have less similarity with biological maps (Figure 1E).
The implication of our work is twofold. From a neuroscience perspective, the experimental results can be viewed as evidence for the hypothesis of hexagonal lattice structure of orientation maps . From a computational simulation perspective, inheriting the major advantages of GCAL, our model is more suited for large scale simulation. Once the lattice structures are obtained by training a relatively small cortical slice on natural image patches, they can be tiled to form an arbitrarily large cortex area, making our model scale gracefully when simulating large cortical areas or even being used as a feature extracting module in computer vision applications.
This work is supported by National Natural Science Foundation of China (61003285, 61202082), the Fundamental Research Funds for the Central Universities (BUPT2012RC0218, BUPT2012RC0219).
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